Search Results for author: Nicolas Goix

Found 5 papers, 2 papers with code

How to Evaluate the Quality of Unsupervised Anomaly Detection Algorithms?

2 code implementations5 Jul 2016 Nicolas Goix

When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.

Supervised Anomaly Detection Unsupervised Anomaly Detection

Sparsity in Multivariate Extremes with Applications to Anomaly Detection

no code implementations21 Jul 2015 Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e. g. portfolio monitoring, insurance, environmental risk management and anomaly detection.

Anomaly Detection Dimensionality Reduction +1

On Anomaly Ranking and Excess-Mass Curves

no code implementations5 Feb 2015 Nicolas Goix, Anne Sabourin, Stéphan Clémençon

Extensions to the multivariate setting are far from straightforward and it is precisely the main purpose of this paper to introduce a novel and convenient (functional) criterion for measuring the performance of a scoring function regarding the anomaly ranking task, referred to as the Excess-Mass curve (EM curve).

Anomaly Detection

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